pytorch实现分类预测
时间: 2023-06-29 22:09:35 浏览: 45
分类预测是机器学习中的一个重要任务,PyTorch是一种流行的深度学习框架,可以用于实现分类预测模型。下面是一个简单的分类预测代码示例:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
# 定义数据预处理
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])
# 加载训练数据集
trainset = datasets.MNIST('data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)
# 定义模型
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2 = nn.Conv2d(32, 64, 3, 1)
self.dropout1 = nn.Dropout2d(0.25)
self.dropout2 = nn.Dropout2d(0.5)
self.fc1 = nn.Linear(9216, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = self.conv1(x)
x = nn.functional.relu(x)
x = self.conv2(x)
x = nn.functional.relu(x)
x = nn.functional.max_pool2d(x, 2)
x = self.dropout1(x)
x = torch.flatten(x, 1)
x = self.fc1(x)
x = nn.functional.relu(x)
x = self.dropout2(x)
x = self.fc2(x)
output = nn.functional.log_softmax(x, dim=1)
return output
net = Net()
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
# 训练模型
for epoch in range(10):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 100 == 99:
print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
running_loss = 0.0
print('Finished Training')
# 加载测试数据集
testset = datasets.MNIST('data', train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False)
# 在测试集上评估模型
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))
```
在这段代码中,我们定义了一个简单的卷积神经网络作为分类器,使用交叉熵损失函数和随机梯度下降优化器进行模型训练,最后在测试集上评估了模型的准确率。
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